Use Patterns and Challenges of the Social Media Platform X Among Physiotherapists in Saudi Arabia: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Social media platforms have become salient channels for healthcare professionals' continuous education and professional development. Among them, X (formerly Twitter) is used by physiotherapists for engaging in evidence-based discussions and accessing emerging research. In Saudi Arabia, a country with a high social media penetration rate, the platform offers unique opportunities and challenges for physiotherapy-related knowledge acquisition and networking. OBJECTIVE: This study aimed to determine how physiotherapists in Saudi Arabia engage in physiotherapy-related debates on X, explore their usage patterns, and identify associated challenges and perceived professional benefits. METHODS: We conducted a cross-sectional online survey among licensed physiotherapists registered with the Saudi Commission for Health Specialties. The questionnaire covered demographic data, social media usage, interaction patterns, perceived challenges, and motivations for use. Descriptive statistics and chi-square tests were used to examine demographic data, usage patterns, challenges and concerns, perceived professional benefits, as well as the association between demographic characteristics and usage patterns. Statistical significance was set at p < .05. RESULTS: Out of 193 responses, 188 were valid and included in data analysis. Among the respondents, 76.06% reported having an active account on X. Most respondents were female (57.98%) and aged 31-40 years (42.02%). The time spent on the platform varied, with 32.87% spending 4-6 hours a week and 27.27% spending less than an hour per week. Respondents' interaction extent was moderate, with 35.66% reporting occasional interaction. The respondents mainly interacted with knowledge-sharing posts (72.34%), followed by training/workshop-related posts (66.66%). The respondents reported difficulty in finding reliable information (52.45%), time constraints (40.56%), communication barriers (48.25%), and conflicts of interest (51.74%) as challenges concerning engaging in physiotherapy-related debates on X. Despite these concerns, many respondents acknowledged the platform's value, as 60.14% agreed it helped them stay updated with emerging research, 68.53% believed it fostered knowledge sharing, and 67.83% believed it enhanced critical thinking among the community. CONCLUSIONS: Physiotherapists in Saudi Arabia demonstrate active engagement with physiotherapy-related content on X for professional development. While the platform offers valuable opportunities for learning and collaboration, notable barriers, such as information credibility and time limitations, must be addressed. Enhancing digital literacy and establishing clear guidelines for professional social media use may help maximize the platform's potential as a tool for continuous development in physiotherapy practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".